Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations12287
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory104.0 B

Variable types

Numeric10
Categorical3

Alerts

dec is highly overall correlated with objidHigh correlation
g is highly overall correlated with i and 3 other fieldsHigh correlation
i is highly overall correlated with g and 2 other fieldsHigh correlation
objid is highly overall correlated with decHigh correlation
petror90_r is highly overall correlated with g and 1 other fieldsHigh correlation
r is highly overall correlated with g and 3 other fieldsHigh correlation
spiral is highly overall correlated with uncertainHigh correlation
u is highly overall correlated with g and 2 other fieldsHigh correlation
uncertain is highly overall correlated with spiralHigh correlation

Reproduction

Analysis started2024-07-26 13:45:01.538229
Analysis finished2024-07-26 13:45:10.663018
Duration9.12 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

objid
Real number (ℝ)

HIGH CORRELATION 

Distinct12278
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.237658 × 1018
Minimum1.2376487 × 1018
Maximum1.2376747 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.1 KiB
2024-07-26T08:45:10.746740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.2376487 × 1018
5-th percentile1.2376487 × 1018
Q11.2376518 × 1018
median1.2376557 × 1018
Q31.2376622 × 1018
95-th percentile1.237672 × 1018
Maximum1.2376747 × 1018
Range2.5982937 × 1013
Interquartile range (IQR)1.0377989 × 1013

Descriptive statistics

Standard deviation6.7996122 × 1012
Coefficient of variation (CV)5.4939347 × 10-6
Kurtosis-0.047130061
Mean1.237658 × 1018
Median Absolute Deviation (MAD)4.8699499 × 1012
Skewness0.74050304
Sum1.5207104 × 1022
Variance4.6234726 × 1025
MonotonicityNot monotonic
2024-07-26T08:45:10.867209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.237654607 × 10182
 
< 0.1%
1.237651737 × 10182
 
< 0.1%
1.23765488 × 10182
 
< 0.1%
1.237660412 × 10182
 
< 0.1%
1.237654031 × 10182
 
< 0.1%
1.237662247 × 10182
 
< 0.1%
1.237658423 × 10182
 
< 0.1%
1.237658425 × 10182
 
< 0.1%
1.237661951 × 10182
 
< 0.1%
1.237658492 × 10181
 
< 0.1%
Other values (12268) 12268
99.8%
ValueCountFrequency (%)
1.237648673 × 10181
< 0.1%
1.237648674 × 10181
< 0.1%
1.237648674 × 10181
< 0.1%
1.237648675 × 10181
< 0.1%
1.237648675 × 10181
< 0.1%
1.237648675 × 10181
< 0.1%
1.237648675 × 10181
< 0.1%
1.237648703 × 10181
< 0.1%
1.237648703 × 10181
< 0.1%
1.237648703 × 10181
< 0.1%
ValueCountFrequency (%)
1.237674656 × 10181
< 0.1%
1.237674656 × 10181
< 0.1%
1.237674655 × 10181
< 0.1%
1.237674655 × 10181
< 0.1%
1.237674655 × 10181
< 0.1%
1.237674655 × 10181
< 0.1%
1.237674654 × 10181
< 0.1%
1.237674654 × 10181
< 0.1%
1.237674654 × 10181
< 0.1%
1.237674652 × 10181
< 0.1%

nvote
Real number (ℝ)

Distinct72
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.62912
Minimum10
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.1 KiB
2024-07-26T08:45:10.970033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile23
Q128
median33
Q351
95-th percentile65
Maximum82
Range72
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.846837
Coefficient of variation (CV)0.35845592
Kurtosis-0.54601574
Mean38.62912
Median Absolute Deviation (MAD)7
Skewness0.79734954
Sum474636
Variance191.73489
MonotonicityNot monotonic
2024-07-26T08:45:11.079696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 650
 
5.3%
29 642
 
5.2%
31 631
 
5.1%
28 598
 
4.9%
32 596
 
4.9%
27 566
 
4.6%
33 510
 
4.2%
26 478
 
3.9%
34 455
 
3.7%
35 414
 
3.4%
Other values (62) 6747
54.9%
ValueCountFrequency (%)
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 3
 
< 0.1%
15 5
 
< 0.1%
16 19
 
0.2%
17 16
 
0.1%
18 45
0.4%
19 64
0.5%
20 89
0.7%
ValueCountFrequency (%)
82 1
 
< 0.1%
81 1
 
< 0.1%
80 1
 
< 0.1%
79 5
 
< 0.1%
78 7
 
0.1%
77 9
 
0.1%
76 14
0.1%
75 15
0.1%
74 17
0.1%
73 23
0.2%

spiral
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size600.1 KiB
0
7022 
1
5265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 7022
57.1%
1 5265
42.9%

Length

2024-07-26T08:45:11.181833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-26T08:45:11.257548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 7022
57.1%
1 5265
42.9%

Most occurring characters

ValueCountFrequency (%)
0 7022
57.1%
1 5265
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7022
57.1%
1 5265
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7022
57.1%
1 5265
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7022
57.1%
1 5265
42.9%

elliptical
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size600.1 KiB
0
9340 
1
2947 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9340
76.0%
1 2947
 
24.0%

Length

2024-07-26T08:45:11.333905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-26T08:45:11.403161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 9340
76.0%
1 2947
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 9340
76.0%
1 2947
 
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9340
76.0%
1 2947
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9340
76.0%
1 2947
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9340
76.0%
1 2947
 
24.0%

uncertain
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size600.1 KiB
0
8212 
1
4075 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8212
66.8%
1 4075
33.2%

Length

2024-07-26T08:45:11.479293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-26T08:45:11.550558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 8212
66.8%
1 4075
33.2%

Most occurring characters

ValueCountFrequency (%)
0 8212
66.8%
1 4075
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8212
66.8%
1 4075
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8212
66.8%
1 4075
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8212
66.8%
1 4075
33.2%

ra
Real number (ℝ)

Distinct12278
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.26488
Minimum119.70954
Maximum248.18391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.1 KiB
2024-07-26T08:45:11.637364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum119.70954
5-th percentile133.82117
Q1162.53604
median189.68927
Q3216.47319
95-th percentile234.7411
Maximum248.18391
Range128.47437
Interquartile range (IQR)53.937147

Descriptive statistics

Standard deviation32.307552
Coefficient of variation (CV)0.1716069
Kurtosis-1.0636174
Mean188.26488
Median Absolute Deviation (MAD)26.962916
Skewness-0.17994411
Sum2313210.6
Variance1043.7779
MonotonicityNot monotonic
2024-07-26T08:45:11.785699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183.1376821 2
 
< 0.1%
213.5957917 2
 
< 0.1%
198.4660514 2
 
< 0.1%
138.588716 2
 
< 0.1%
166.3605633 2
 
< 0.1%
209.5839626 2
 
< 0.1%
177.6260604 2
 
< 0.1%
176.2730082 2
 
< 0.1%
235.7312376 2
 
< 0.1%
188.1184096 1
 
< 0.1%
Other values (12268) 12268
99.8%
ValueCountFrequency (%)
119.7095358 1
< 0.1%
119.8597716 1
< 0.1%
119.9105983 1
< 0.1%
120.1650589 1
< 0.1%
120.1802024 1
< 0.1%
120.1922839 1
< 0.1%
120.3781158 1
< 0.1%
120.3937082 1
< 0.1%
120.400965 1
< 0.1%
120.4423862 1
< 0.1%
ValueCountFrequency (%)
248.1839079 1
< 0.1%
248.0209498 1
< 0.1%
247.8003051 1
< 0.1%
247.7783207 1
< 0.1%
247.2021563 1
< 0.1%
247.1199908 1
< 0.1%
247.077152 1
< 0.1%
246.9772498 1
< 0.1%
246.9382393 1
< 0.1%
246.9248501 1
< 0.1%

dec
Real number (ℝ)

HIGH CORRELATION 

Distinct12278
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1783261
Minimum-3.7289935
Maximum9.9997066
Zeros0
Zeros (%)0.0%
Negative1972
Negative (%)16.0%
Memory size96.1 KiB
2024-07-26T08:45:11.941883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.7289935
5-th percentile-2.0352662
Q11.2428202
median4.3715158
Q37.3464072
95-th percentile9.486792
Maximum9.9997066
Range13.7287
Interquartile range (IQR)6.103587

Descriptive statistics

Standard deviation3.6311655
Coefficient of variation (CV)0.86904789
Kurtosis-0.99960444
Mean4.1783261
Median Absolute Deviation (MAD)3.0491538
Skewness-0.22679547
Sum51339.092
Variance13.185363
MonotonicityNot monotonic
2024-07-26T08:45:12.111198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.076856564 2
 
< 0.1%
2.978021294 2
 
< 0.1%
4.297308051 2
 
< 0.1%
6.870832209 2
 
< 0.1%
3.421162994 2
 
< 0.1%
7.225087329 2
 
< 0.1%
6.94198728 2
 
< 0.1%
8.469523471 2
 
< 0.1%
9.228520048 2
 
< 0.1%
9.243606437 1
 
< 0.1%
Other values (12268) 12268
99.8%
ValueCountFrequency (%)
-3.728993456 1
< 0.1%
-3.709671388 1
< 0.1%
-3.708275699 1
< 0.1%
-3.707178831 1
< 0.1%
-3.696449354 1
< 0.1%
-3.696313251 1
< 0.1%
-3.687864061 1
< 0.1%
-3.682686892 1
< 0.1%
-3.679478654 1
< 0.1%
-3.678621113 1
< 0.1%
ValueCountFrequency (%)
9.999706617 1
< 0.1%
9.9961496 1
< 0.1%
9.995553668 1
< 0.1%
9.995329181 1
< 0.1%
9.994284924 1
< 0.1%
9.993891377 1
< 0.1%
9.991507065 1
< 0.1%
9.990768244 1
< 0.1%
9.990416356 1
< 0.1%
9.990285956 1
< 0.1%

petror90_r
Real number (ℝ)

HIGH CORRELATION 

Distinct12146
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.059717
Minimum10.00003
Maximum243.0828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.1 KiB
2024-07-26T08:45:12.227479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10.00003
5-th percentile10.141443
Q110.829195
median12.12908
Q314.805175
95-th percentile23.430055
Maximum243.0828
Range233.08277
Interquartile range (IQR)3.97598

Descriptive statistics

Standard deviation7.1603742
Coefficient of variation (CV)0.50928294
Kurtosis216.48596
Mean14.059717
Median Absolute Deviation (MAD)1.5928
Skewness10.408472
Sum172751.75
Variance51.270959
MonotonicityNot monotonic
2024-07-26T08:45:12.336726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.02207 3
 
< 0.1%
12.41411 2
 
< 0.1%
10.14613 2
 
< 0.1%
17.73321 2
 
< 0.1%
11.00344 2
 
< 0.1%
10.50061 2
 
< 0.1%
11.91953 2
 
< 0.1%
13.75332 2
 
< 0.1%
11.66383 2
 
< 0.1%
10.10332 2
 
< 0.1%
Other values (12136) 12266
99.8%
ValueCountFrequency (%)
10.00003 1
< 0.1%
10.00005 1
< 0.1%
10.00011 1
< 0.1%
10.00035 1
< 0.1%
10.00058 1
< 0.1%
10.00145 1
< 0.1%
10.00177 1
< 0.1%
10.00193 1
< 0.1%
10.00229 1
< 0.1%
10.00244 1
< 0.1%
ValueCountFrequency (%)
243.0828 1
< 0.1%
231.1218 1
< 0.1%
157.0176 1
< 0.1%
150.2384 1
< 0.1%
126.9922 1
< 0.1%
124.8568 1
< 0.1%
118.2117 1
< 0.1%
116.8821 1
< 0.1%
107.7707 1
< 0.1%
100.3658 1
< 0.1%

expAB_r
Real number (ℝ)

Distinct12241
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62318581
Minimum0.05
Maximum0.9999983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.1 KiB
2024-07-26T08:45:12.439799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.21183214
Q10.4632574
median0.662973
Q30.8095368
95-th percentile0.92830867
Maximum0.9999983
Range0.9499983
Interquartile range (IQR)0.3462794

Descriptive statistics

Standard deviation0.22263451
Coefficient of variation (CV)0.35725222
Kurtosis-0.79530298
Mean0.62318581
Median Absolute Deviation (MAD)0.1614991
Skewness-0.447677
Sum7657.0841
Variance0.049566126
MonotonicityNot monotonic
2024-07-26T08:45:12.546119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 17
 
0.1%
0.2500007 3
 
< 0.1%
0.4100322 2
 
< 0.1%
0.2000009 2
 
< 0.1%
0.199956 2
 
< 0.1%
0.3500022 2
 
< 0.1%
0.7835927 2
 
< 0.1%
0.6054484 2
 
< 0.1%
0.5771658 2
 
< 0.1%
0.7386402 2
 
< 0.1%
Other values (12231) 12251
99.7%
ValueCountFrequency (%)
0.05 17
0.1%
0.05205885 1
 
< 0.1%
0.07250749 1
 
< 0.1%
0.09999482 1
 
< 0.1%
0.1013182 1
 
< 0.1%
0.1037414 1
 
< 0.1%
0.1055809 1
 
< 0.1%
0.1057831 1
 
< 0.1%
0.1062607 1
 
< 0.1%
0.1064134 1
 
< 0.1%
ValueCountFrequency (%)
0.9999983 1
< 0.1%
0.9999921 1
< 0.1%
0.9999917 1
< 0.1%
0.9999601 1
< 0.1%
0.9999282 1
< 0.1%
0.9999271 1
< 0.1%
0.9999145 1
< 0.1%
0.9998941 1
< 0.1%
0.9998832 1
< 0.1%
0.9998824 1
< 0.1%

u
Real number (ℝ)

HIGH CORRELATION 

Distinct12063
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.147065
Minimum8.060121
Maximum29.42589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.1 KiB
2024-07-26T08:45:12.652370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8.060121
5-th percentile16.26787
Q117.350055
median18.05763
Q318.767205
95-th percentile20.367216
Maximum29.42589
Range21.365769
Interquartile range (IQR)1.41715

Descriptive statistics

Standard deviation1.3453295
Coefficient of variation (CV)0.074134829
Kurtosis7.5300245
Mean18.147065
Median Absolute Deviation (MAD)0.70878
Skewness1.3415337
Sum222972.99
Variance1.8099116
MonotonicityNot monotonic
2024-07-26T08:45:12.752214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.35346 3
 
< 0.1%
18.16707 3
 
< 0.1%
19.52535 3
 
< 0.1%
18.16183 2
 
< 0.1%
16.54961 2
 
< 0.1%
17.95578 2
 
< 0.1%
18.57583 2
 
< 0.1%
18.97404 2
 
< 0.1%
18.25848 2
 
< 0.1%
18.87083 2
 
< 0.1%
Other values (12053) 12264
99.8%
ValueCountFrequency (%)
8.060121 1
< 0.1%
9.145649 1
< 0.1%
10.3679 1
< 0.1%
10.66987 1
< 0.1%
11.89966 1
< 0.1%
12.60942 1
< 0.1%
12.85339 1
< 0.1%
12.88416 1
< 0.1%
12.97762 1
< 0.1%
13.00258 1
< 0.1%
ValueCountFrequency (%)
29.42589 1
< 0.1%
29.40972 1
< 0.1%
28.64978 1
< 0.1%
28.56798 1
< 0.1%
28.10846 1
< 0.1%
27.62654 1
< 0.1%
27.35838 1
< 0.1%
27.33539 1
< 0.1%
27.31128 1
< 0.1%
27.18354 1
< 0.1%

g
Real number (ℝ)

HIGH CORRELATION 

Distinct12082
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.470099
Minimum8.236569
Maximum28.05585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.1 KiB
2024-07-26T08:45:12.851522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8.236569
5-th percentile14.706084
Q115.78604
median16.44064
Q317.1411
95-th percentile18.200847
Maximum28.05585
Range19.819281
Interquartile range (IQR)1.35506

Descriptive statistics

Standard deviation1.135328
Coefficient of variation (CV)0.068932672
Kurtosis9.6009475
Mean16.470099
Median Absolute Deviation (MAD)0.67717
Skewness1.0584187
Sum202368.11
Variance1.2889696
MonotonicityNot monotonic
2024-07-26T08:45:12.961574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.26263 3
 
< 0.1%
16.30359 3
 
< 0.1%
16.31939 2
 
< 0.1%
16.34841 2
 
< 0.1%
15.93336 2
 
< 0.1%
15.52971 2
 
< 0.1%
16.49604 2
 
< 0.1%
13.60418 2
 
< 0.1%
17.19862 2
 
< 0.1%
16.81203 2
 
< 0.1%
Other values (12072) 12265
99.8%
ValueCountFrequency (%)
8.236569 1
< 0.1%
8.985449 1
< 0.1%
12.39236 1
< 0.1%
12.42013 1
< 0.1%
12.47631 1
< 0.1%
12.50193 1
< 0.1%
12.57766 1
< 0.1%
12.63298 1
< 0.1%
12.80376 1
< 0.1%
12.88785 1
< 0.1%
ValueCountFrequency (%)
28.05585 1
< 0.1%
27.94282 1
< 0.1%
27.35944 1
< 0.1%
27.26381 1
< 0.1%
27.02163 1
< 0.1%
26.95738 1
< 0.1%
26.66645 1
< 0.1%
26.64876 1
< 0.1%
26.43438 1
< 0.1%
26.07533 1
< 0.1%

r
Real number (ℝ)

HIGH CORRELATION 

Distinct12086
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.681556
Minimum7.406818
Maximum18.60007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.1 KiB
2024-07-26T08:45:13.072822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7.406818
5-th percentile13.950105
Q115.041755
median15.68049
Q316.388135
95-th percentile17.325752
Maximum18.60007
Range11.193252
Interquartile range (IQR)1.34638

Descriptive statistics

Standard deviation1.0219033
Coefficient of variation (CV)0.065165936
Kurtosis1.0380106
Mean15.681556
Median Absolute Deviation (MAD)0.67337
Skewness-0.38484377
Sum192679.28
Variance1.0442863
MonotonicityNot monotonic
2024-07-26T08:45:13.180910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.46082 3
 
< 0.1%
16.16945 3
 
< 0.1%
16.14091 3
 
< 0.1%
16.41709 3
 
< 0.1%
16.37461 2
 
< 0.1%
15.61271 2
 
< 0.1%
14.53724 2
 
< 0.1%
15.62199 2
 
< 0.1%
15.83766 2
 
< 0.1%
14.61514 2
 
< 0.1%
Other values (12076) 12263
99.8%
ValueCountFrequency (%)
7.406818 1
< 0.1%
7.861018 1
< 0.1%
8.617517 1
< 0.1%
8.848534 1
< 0.1%
9.263449 1
< 0.1%
9.503851 1
< 0.1%
11.4944 1
< 0.1%
11.51303 1
< 0.1%
11.56767 1
< 0.1%
11.82123 1
< 0.1%
ValueCountFrequency (%)
18.60007 1
< 0.1%
18.5167 1
< 0.1%
18.45914 1
< 0.1%
18.43695 1
< 0.1%
18.39835 1
< 0.1%
18.36972 1
< 0.1%
18.2617 1
< 0.1%
18.24247 1
< 0.1%
18.23374 1
< 0.1%
18.19725 1
< 0.1%

i
Real number (ℝ)

HIGH CORRELATION 

Distinct12081
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.340306
Minimum7.284586
Maximum27.88026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.1 KiB
2024-07-26T08:45:13.283973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7.284586
5-th percentile13.570022
Q114.66647
median15.30474
Q316.043005
95-th percentile17.025636
Maximum27.88026
Range20.595674
Interquartile range (IQR)1.376535

Descriptive statistics

Standard deviation1.1313742
Coefficient of variation (CV)0.07375174
Kurtosis12.710052
Mean15.340306
Median Absolute Deviation (MAD)0.68509
Skewness1.1999417
Sum188486.33
Variance1.2800076
MonotonicityNot monotonic
2024-07-26T08:45:13.382701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.27555 3
 
< 0.1%
15.50948 2
 
< 0.1%
15.60249 2
 
< 0.1%
15.78922 2
 
< 0.1%
14.57713 2
 
< 0.1%
17.07409 2
 
< 0.1%
15.58189 2
 
< 0.1%
13.83605 2
 
< 0.1%
14.95599 2
 
< 0.1%
14.80717 2
 
< 0.1%
Other values (12071) 12266
99.8%
ValueCountFrequency (%)
7.284586 1
< 0.1%
7.871393 1
< 0.1%
8.424062 1
< 0.1%
11.04099 1
< 0.1%
11.14058 1
< 0.1%
11.40071 1
< 0.1%
11.50133 1
< 0.1%
11.50262 1
< 0.1%
11.63305 1
< 0.1%
11.69665 1
< 0.1%
ValueCountFrequency (%)
27.88026 1
< 0.1%
27.81611 1
< 0.1%
27.11193 1
< 0.1%
26.62337 1
< 0.1%
26.47976 1
< 0.1%
26.25667 1
< 0.1%
26.02874 1
< 0.1%
25.87345 1
< 0.1%
25.51111 1
< 0.1%
25.44893 1
< 0.1%

Interactions

2024-07-26T08:45:09.698958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:02.331102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.219787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.970956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.950852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.692539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.419169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.174923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.883021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.673028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:09.770882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:02.433997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.296704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.046176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.023059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.764847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.492748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.245734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.962452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.744661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:09.845547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:02.525443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.376374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.135913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.100777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.840434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.567282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.319898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.041564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.820817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:09.919579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:02.606400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.452160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.219141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.175406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.915258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.637893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.392119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.124780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.893089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:09.995080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:02.692775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.530793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.297383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.256308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.992873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.712214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.466761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.208575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.966955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:10.064259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:02.796324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.606588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.368338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.328676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.063711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.782696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.536075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.286201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:09.039261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:10.132714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:02.904267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.679537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.438488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.400073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.134068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.891312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.606376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.362521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:09.115456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:10.199178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:02.997295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.749019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.507174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.470924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.202499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.960965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.671002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.435770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:09.481912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:10.276316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.079907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.828282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.591769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.552961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.282430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.039204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.751182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.523071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:09.560961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:10.341735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.153697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:03.901144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:04.662253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:05.625673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:06.353081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.109130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:07.819836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:08.601032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-26T08:45:09.632904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-07-26T08:45:13.452896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
decellipticalexpAB_rginvoteobjidpetror90_rrraspiraluuncertain
dec1.0000.031-0.026-0.018-0.006-0.0030.5930.029-0.0100.0010.015-0.0280.000
elliptical0.0311.0000.4210.0700.1240.0260.0000.0000.1140.0280.4860.1450.395
expAB_r-0.0260.4211.000-0.054-0.097-0.005-0.027-0.012-0.088-0.0390.4490.0040.227
g-0.0180.070-0.0541.0000.945-0.007-0.016-0.5060.963-0.0540.1750.9240.163
i-0.0060.124-0.0970.9451.000-0.010-0.004-0.4880.987-0.0390.0720.7850.184
nvote-0.0030.026-0.005-0.007-0.0101.0000.0040.010-0.010-0.0050.0270.0000.046
objid0.5930.000-0.027-0.016-0.0040.0041.0000.018-0.0080.1150.027-0.0240.010
petror90_r0.0290.000-0.012-0.506-0.4880.0100.0181.000-0.5110.0150.036-0.4580.058
r-0.0100.114-0.0880.9630.987-0.010-0.008-0.5111.000-0.0460.1140.8230.206
ra0.0010.028-0.039-0.054-0.039-0.0050.1150.015-0.0461.0000.041-0.0540.011
spiral0.0150.4860.4490.1750.0720.0270.0270.0360.1140.0411.0000.2340.610
u-0.0280.1450.0040.9240.7850.000-0.024-0.4580.823-0.0540.2341.0000.137
uncertain0.0000.3950.2270.1630.1840.0460.0100.0580.2060.0110.6100.1371.000

Missing values

2024-07-26T08:45:10.436417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-26T08:45:10.588664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

objidnvotespiralellipticaluncertainradecpetror90_rexpAB_rugri
0123764867399267159240010237.945144-0.10517013.363100.92356717.6067715.5298614.6285314.21786
1123764867452947694652010237.7982990.31962411.135600.72922918.6042716.5923315.6466015.23424
2123764867452947699347100237.8114180.29300610.641840.51933217.7110216.2921815.6351115.30861
3123764867506667555633100238.4892100.68809010.568500.54214217.1454815.7821815.1497314.77505
4123764870406334084065100236.173157-0.25361114.087930.19318717.8577116.5557016.0022615.70330
5123764870406373416864100237.125387-0.36707810.435470.20746618.6513017.2141816.5315216.14213
6123764870460034311360100236.4652590.03521915.212020.23132719.3704117.5152216.8071216.40449
7123764870460067076129100237.3303660.18266728.327150.37390516.7914014.8835213.9905613.53973
8123764870460073599662001237.4980660.20199424.875610.48258216.2716414.4034913.5644713.15807
9123764870460086700251100237.7781590.08697811.079770.41857119.5075417.6722216.7131016.25407
objidnvotespiralellipticaluncertainradecpetror90_rexpAB_rugri
12277123767126603445047635001191.4196151.97530213.062380.46135817.4636715.7185914.8972014.50486
12278123767126657125595121100191.1678841.84860212.092910.53883017.7441815.7327014.8574414.43248
12279123767126710786481534100190.6696801.34572030.955530.63480415.9455414.3925213.7983713.46101
12280123767126710786491826100190.6983241.37810710.502380.45693218.1877117.2521516.9861116.77859
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